key: cord-0041699-frcyctys authors: Tatian, Mohammadreza; Arzani, Hossein; Reihan, Majid Karimpour; Bahmanyar, Mohammad Ali; Jalilvand, Hamid title: Effect of soil and physiographic factors on ecological plant groups in the eastern Elborz mountain rangeland of Iran date: 2010-06-02 journal: Grassl DOI: 10.1111/j.1744-697x.2010.00178.x sha: bced1584d557b2bd59e0363d75555b03d831f520 doc_id: 41699 cord_uid: frcyctys To investigate the cause of differences among ecological plant groups in the east of the Elborz mountain rangeland, the role of edaphical and topographical characteristics was considered. Two ordination techniques, detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA), were used. The values of slope, aspect, altitude and lithology information were provided by Geographic Information System (GIS), and geomorphological land units were determined by intersection of overlaid data layers. Plant sampling was undertaken within nine land units with similar lithology and altitude but which differed in slope and aspect, using 30 randomly selected 1 m(2) plots per land unit. Soil samples were taken from two depths (0–20 and 20–50 cm) in each plot. Organic matter, bulk density, texture, calcium carbonate, total nitrogen and available phosphorus and potassium contents were determined. The results indicated that plant species have different responses to edaphical and topographical parameters. The invader species group had a balanced amount of influence from all soil components and topographic factors, whereas the native grasses were located in productive soils, which typically have a low grazing intensity, such as the north facing slopes. Coniferous bushy trees, cushion plants and some shrub plant groups were found on steep slopes with alkaline soils. The broad‐leaved bushy trees plant group was abundant in fine texture soils on low and humid slopes. The appearance of a plant group in a given area isn't accidental, but occurs in response to changes in climatic, topographic, edaphic and biotic parameters. In fact, vegetation groups are determined by the combined effects of a whole range of ecological factors. Thus, change in the soil, topography and grazing factors can lead to vegetation responses in each area of the landscape (Holechek et al. 1989) . Jangman et al. (1995) showed that vegetation ordination can be used to generate the ecological factor classes in each site. Plant species that appear in similar regions have equal ecologic requirements and can be defined as ''ecological species groups''. The regions that contain similar ecological species groups create ecological groups that are homogeneous habitats with similar ecologic and floristic composition, which can be used in habitat classification (Zahedi and Mohammadi 2002; Kashian 2003; Kooch et al. 2008) . Ecologists use multivariate analyses to understand and identify complex relationships between ecological factors and their interactions in ecosystems. Some of these methods for investigation of relationships between plant species or communities with ecological parameters are direct and indirect gradient analyses (Jangman et al. 1987; Zahedi 1998; Villers-ruiz et al. 2003; Jalilvand et al. 2007 ). In direct gradient analysis, ecological data ordination is based on vegetation data and plant species distribution in relation to ecological parameters, whereas indirect gradient analysis is based on floristic data analysis without the effect of ecological factors. The ecological factors will be apparent only after analysis of floristic change. The ecological data are entered into the analysis in the interpretation step (Austin 1968; Moghaddam 2001; Jafarian Jelodar 2008) . Use of new numerical methods and development of ordination techniques has improved understanding of plant communities and ecological parameter interactions. Nowadays, detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA) techniques have been identified as important and practical methods of direct and indirect gradient analyses (Mesdaghi 2005) . The DCA method is an indirect method to show species correlation. This method arranges vegetation data based on their presence or absence in sampling plots and shows species group locations at each site. First, this method calculates the magnitude of species composition change in the first ordination axis (lengths of gradient). Based on the calculated gradient, the type of analysis to be used for ordination is determined ( ‡3 for CCA, <3 for RDA). Species group separation is clearer in DCA. Furthermore, a group's border isn't always identifiable in CCA or RDA. Although, for determining the relationship between species and environmental data, direct methods are the best methods of ordination, DCA is more useful in species grouping (Hill and Gauch 1980; Jean and Bouchard 1993) . The combination of CCA and RDA is the complement of DCA. These new techniques select the linear combination of environmental variables that maximize species scores, whereas CCA chooses the best weights for the environmental variables (Zahedi 1998) . This gives the first CCA axis. In CCA, composite gradients are linear combinations of environmental variables, giving a much simpler analysis that provides a summary of the species-environment relationships. In vector diagrams, vector lengths show the efficacy of each factor, with a longer length indicating a stronger relationship. Also, closeness of species to a vector shows its responsiveness to that factor. Occurrence of a species near a vector indicates a positive relationship with that factor and its diminished relationship with the other factors (Jangman et al. 1987; Ghorbani et al. 2003) . Use of these methods in different studies has defined associations of plant species that have been used to create ecological groups in response to a complex collection of soil characteristics (Bui and Henderson 2003; Jafari et al. 2004; Abd El-Ghani and El-Sawaf 2005) or a mixture of physiographic, climatic and edaphic factors (Clark and Mann 1999; Sneddon 2001; Evens and Levin 2004; Mohtasham nia et al. 2008) . Grazing management and plant competition also have important roles in determining inter-species associations (Marc et al. 2003; Lenssen et al. 2004 ). The ecologi-cal grouping method has been used in several ecosystems such as rain and temperate forests, coastal plains, desert regions and wetlands (Clark and Mann 1999; Diane and Maureen 2004; Omer 2004; Monier 2006; Päivi 2006; Li et al. 2008) or in long-term studies (Lameire 2000) . Interactions between plant groups and environmental parameters provide a useful opportunity to alter management to improve rangeland ecosystems. Thus, DCA and CCA multivariate analyses were used in this research to investigate the effect of edaphic and physiographic factors that formed ecological plant groups in the east of the Elborz mountain rangeland of Iran. The main purpose of the present study was to determine the strongest factors affecting the separation of plant groups. Identification of these parameters in a given ecosystem helps us to apply appropriate management for restoration and development in the present and in similar regions. The Elborz Mountains are located near the south of the Caspian Sea in northern Iran. The site studied is a cold and semirid summer rangeland area in the east of the Elborz mountain range (54°04¢30¢¢-54°09¢10¢¢W, 36°29¢28¢¢-36°31¢45¢¢N; Figure 1 ). Mean annual precipitation is 380 mm of which 70% is snow, and mean annual temperature is 12.4°C. Elevation ranges between 2000 and 2700 m a.s.l. Vegetation comprises grasses, shrubs and bushy trees and some cultivated plants on the rangeland borders. To investigate the relationship between vegetation, and physiographic and edaphic characteristics, topographical (1 : 25,000) and lithological maps (1 : 100,000) of the area were used in the first step. Then, the information layers of slope, aspect, hypsography and lithology were created in GIS (Arc View 3.3). The geomorphologic land units (n = 66) map was obtained by overlaying the slope, aspect, hypsometric and lithologic layers ( Figure 2 ). From these land units nine, with similar lithology and altitude (2100-2200 m a.s.l.) but contrasting slopes and aspects, were selected. Some of the selected units shown in Figure 2 were merged because of their close similarities in most factors. Except in one land unit where 40 plots were sampled, sampling was conducted at 30 randomly chosen plots of 1 m 2 within each of the nine land units. Size and number of plots were obtained by minimal area and statistical methods, respectively (Kent and Coker 1996) . Topographic (slope and aspect) and vegetative characteristics (species names, cover, density and frequency percentage) were recorded by visual scores and subsequent calculations within each plot. Aspect data were calculated by angle conversion to northing; Heat Load = [1 ) cos(h ) 45)] ⁄ 2 where h is the aspect value based on 360°with the highest heat being in the southwest quadrant. Soil samples were taken from 0-20 cm and 20-50 cm depths in each plot. Sub samples from well-mixed soil layers were analyzed for texture (Gee and Bauder 1982) , calcium carbonate (Nelson 1982) , organic matter (Nelson and Sommers 1982) , bulk density (Black and Hartge 1982) , total nitrogen (Bremner and Mulvaney 1982) , available phosphorus (Olsen and Sommers 1982) and available potassium (Knudson and Peterson 1982) . For data analysis, DCA, CCA techniques and partial variance analysis were used in Canoco 4.0 software (Mesdaghi 2005) . Where length of gradient in DCA analysis was ‡3, then CCA was used for further analyses. Interpretation of CCA was done using species grouping in DCA. Significant correlations between species and environment factors were tested by Monte Carlo test (with 99 frequencies), P-value and F-ratio. Where significant relationships were found, two dimensional diagrams of species and environmental para-meters were developed (Jangman et al. 1995) . Finally, the impact of soil, topography and their combined impact on landscape variation were calculated by partial variance analysis (Lepz and Smilauer 2003) . The DCA analysis showed that the components of variation could be expressed in four axes (Table 1 ). The cumulative percentage variance of species data were significant, ranging between 36.5 in Axis 1 to 54.7 in Axis 4. These results revealed that there was a significant difference between the four main plant groups which are well separated by correlation. The species distribution diagram ( Figure 3) shows that the ordination on the first two axes showed separation into four plant groups, with component species being: Group 1 exotic forbs: Centaurea solstitialiss, Cirsium arvense, Eryngium caeruleum, Euphorbia cheiradenia, Gundelia tournefortii, Marrubium vulgare, Phlomis herbaventi, Stachys byzantina, Stachys inflate and Taraxacum vulgare. Group 2 native grasses: Agropyron elongatum, Bromus tomentellus, Bromus tectorum, Stipa barbata, Festuca ovina, Melica persica, Hordeum fragile, Dactylis glomerata and Dianthus orientalis. Group 3 native shrubs, forbs and conifers: Juniperus sabina, Juniperus communis, Acantholimon pterostegium, Acanthophylum crassifolium, Onobrychis cornuta, Astraga-lus vereskensis, Astragalus gossypinus, Astragalus parrowianus, Salvia officinalis, Thymus kotschyanus, Thymus pubescence, Thymus volgare, Verbascum thapsus, Teucrium polium and Achillea millefolium. Group 4 native broadleaf bushy trees: Cerasus pseudoprostrata and Cotoneaster nummularioides. The origin of the species and the abbreviations used in the tables and figures are shown in Appendices I and II. Most of these species are correlated with a positive vector in the first and second axes. Based on species group's distribution, the first and third Groups and a part of the second Group are located within the positive vector axes. The fourth Group is located in the positive vector of the first axis and the negative vector of the second axis (Figure 3) . Aspect was shown in CCA analysis (Table 2) (13, 25, 28, 43, 46 , and 8 and 9 merged, 6 and 37 merged, 18 and 20 merged, and 14, 17 and 19 merged) having similar lithology and altitude, but different slope and aspect were chosen, from which 30 plots were sampled for species composition. Figure 4 ) most likely influenced how species were allocated into the four distinct groups, which is revealed by DCA. In view of the species reaction to the different factors based on their position in the landscape, the results showed that: Group 1 exotic forbs is closest to the origin, indicating that it has a balanced amount of influence from all soil components and GIS factors; Group 2 natives grasses are normally found in places with a northern aspect, flat slope, and with higher K, N and OC; Group 3 native shrubs, forbs and conifers tend to occur on higher slopes, but with a southern aspect with higher Ca and P; Group 4 tends to grow on clay and silt soils, not sandy soil. Results of partial variance (Table 3) showed that 69.6% of rangeland composition changes were explainable by the soil and topographic parameters. Among these, 48.9% belonged to soil, 15.5%, topography and 5.2% common effect of them. The results of DCA analysis show that different plant species are found in contrasting ecologic zones. Environmental factors have an important effect on plant species distribution in this area as they have separated the species into distinct ecological groups. The effect of some environmental factors appeared to influence the presence or absence of some species within plots and their grouping among plots. This DCA analysis approach has also been used in other studies to effectively identify species group's reactions to ecological factors effects (Guisan and Zimmermann 2000; Diane and Maureen 2004; Monier 2006; Li et al. 2008) . The plant species reaction to abiotic factors in CCA analyses show that each species has reacted to one or several edaphic or topographic factors. In this case, the Group 1 invader species demonstrated the least reaction to these factors because the species of this group were all located close to the origin of coordinates and were not along the environmental gradient in two axes. However, these species were located some distance along the soil texture (silt and clay) and bulk density vectors ( Figure 4) . Also, their distribution is inversely related to soil elements (N, P, K), OC, sand, aspect and slope vectors. There were invader species such as Cirsium arvense, Centaurea solstitialis, Gundelia tournefortii, Eryngium caeruleum, Marrubium vulgare, Phlomis herbaventi, Euphorbia cheiradenia, Taraxacum vulgare and two species of Stachys genus in this group that Stachys spp. and Euphorbia cheiradenia are native plants but others are exotic. This is indicative of the intensive grazing pressure on this habitat since grazing animals deplete soil fertility in this environment (Toit et al. 2009 ). Moreover, low P soils on moderate slopes are likely caused by fertility transfer to flatter sites (Arzani et al. 2007 ). Livestock trampling under intensive grazing habitats has also increased soil bulk density (Han et al. 2008) and probably resulted in a mix of topsoil and subsurface soils as seen by the altered physical, chemical and biological properties, each of which can influence seed germination and survival (De Falco et al. 2009 ). On the other hand, the absence of distinct relationships between this group of invader species and abiotic factors is related to the absence of similar environmental factors because the effect of intensive grazing (as a human factor) has invoked other environmental factors, which have rearranged the ecological balance in these sections (Ghorbani et al. 2003; Jafari et al. 2004) . This finding confirms that of Andrieu et al. (2007) which found that invader species become abundant in these landscapes irrespective of local ecological conditions. The species in Group 2 belong to grasses. Abundance of these species is positively influenced by N, K, OC and C ⁄ N of soil, and aspect, but negatively influenced by CaCO 3 (alkaline soils), whereas subsoil bulk density had little impact. Abundance of grass species had no positive correlation to soil texture or slope, as the grasses were indifferent to slopes and soil texture. Because these grass species are sensitive to high grazing pressure, their distribution is restricted to low-intensity grazed landscapes, where soil fertility (nitrogen, potassium and organic matter) is sufficient to support good pasture production (Xie and Wittig 2004) . This is likely because grazing stimulates plant regrowth and absorption of soil nutrients. Also, erosion in intensively grazed landscapes depletes soil elements and decreases soil Correspondence between plant species and environment factors in canonical correspondence analysis (CCA). The further a point is from the origin, the greater is the influence of that factor(s) on the plant's presence. The closer the point is to an axis line, the stronger is the influence of that factor, relative to another factor, on the species presence in that part of the landscape. fertility (Dormaar and Willms 1998; Hosseinzadeh 2006) . Animal treading damage can result in decreased soil porosity (increased soil bulk density). This limits sustainability of sensitive species such as grasses in more heavily grazed sites (Arzani et al. 2007 ). On the other hand, while not measured in this experiment, a higher soil moisture content on northern aspects (in the northern hemisphere) should improve conditions for grass abundance (Taghipour 2005; Mirdavoodi et al. 2007) . High soil pH (high CaCO 3 ) is also a known limiting factor for growth of grasses (Jafari et al. 2003) . In conclusion, we believe the grasses in Group 2 ( Figure 3 ) are so grouped, as a response to high soil fertility, low pH, high soil moisture regime and low grazing intensity. Group 3 includes cushion plants (such as Onobrychis cornuta and Astragalus spp.), coniferous bushy trees (Juniperus sabina and Juniperus communis) and some shrubs (such as Thymus spp.) that are strongly responsive to increasing slope and higher soil pH. The present study confirms that these species are found in steep, alkaline soils (Zarehchahooki et al. 2001; Esmaeelzadeh et al. 2007; Haghian et al. 2008; Mohtasham nia et al. 2008) . Group 4 broadleaf bushy tree species, respond positively to finer soil texture. This response probably relates to a higher soil moisture requirement of these species because they are dominant on north and north-western faces and on low slopes with fine textured soils that hold more water than coarse-textured soils. These landscapes maintain a higher soil moisture regime than southern aspects, causing broadleaved species to abound (Zahedi and Mohammadi 2002; Shokri et al. 2003; Khademolhosseini et al. 2007 ). The analysis of partial variance (Table 3) showed a changing percentage of the factors that we studied impact rangeland composition. Soil (49%) and topographic (15%) factors explain much of the total variance relating to whether ecological groups are present in the landscape. The combined effect of soil and topography explains only a further approximately 5% of the plant groups presence in the landscape. Therefore, Axis 1 was relates to slope, aspect and soil organic matter, while Axis 2 represents soil texture (sand, silt and clay, Figure 4 ). We assume that the remaining variation arose from biotic factors (especially grazing). Although grazing was not considered a variable in these analyses, the effect of grazing on plant composition and soil properties in the landscape is undeniable (Han et al. 2008; Pei et al. 2008) . This grazing effect appears to account for the appearance of Group 1 species as invader species. The present study showed that at one given altitude in this cold and semiarid, mountain rangeland, the effect of aspect and slope and soil properties played an important role in determining plant community composition. Also, an apparently high grazing intensity (increased bulk density and elevated soil fertility status) increased soil compaction and decreased the presence of the native grasses. These changes, with physiographic effects, are important reasons for variations in soil fertility. These effects result in changing vegetation communities, which includes the opportunity for ingress of invader plants. Plant community species alter ecological relationships that can create new landscapes. Identification of these species and understanding of their relationships with environmental parameters can help range management planning. In this way, the use of multivariate analyses (DCA and CCA) can be useful to describe species variation. In new research, grazing intensity should also be measured and used as an environmental variable in these multivariate analyses. 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